Retailers need to start planning for the post COVID phase with a strong strategy on how to recover losses and reopen their business. Understanding what the crisis entails, defining their new normal, and working towards it will help in emerging strongly from the crisis. According to industry sources, the Artificial Intelligence (AI) market in the retail sector is expected to grow by USD 14.05 billion by 2023. AI has the potential to drive unprecedented changes in the retail sector and help them retool for the new world.
Here are the few areas where AI can be instrumental in transforming the retail sector:In the Consumer Space
Targeted Marketing:
Retailers will have to increasingly operate online, reducing lead time, and providing a better experience for their customers. Seasoned customers who commonly don’t shop online could become a valuable demographic as their online shopping activity increases. Dayparting, i.e., dividing the day into several parts and analyzing when shoppers look for a particular product, will help in strategically advertising the products based on the shopping behavior during that time period. AI can help monitor these trends and ensure that retailers tap into the right audiences at the right time and that there is a minimum excess inventory left.
Personalized Visualization:
Enhancing user experience in these difficult times can be instrumental in boosting sales. Fashion e-commerce platforms should facilitate apparel shopping through image search and personalization for their customers. An element that can massively upgrade client experience is the ability to visualize an outfit on a human body.
Generative Adversarial Networks(GANs) can learn and generate data with the same statistics as the training set to transfer customizable outfits and body poses to virtual models or even a virtual avatar of the shopper that he or she has uploaded.

Across the Supply Chain
Inventory Management:
Robots can identify locations in the warehouse where items are out-of-stock. They can then send images of the locations to associates who use handheld devices and also transmit that information to the fast unloader robots to help them prioritize which items get unloaded from trucks first.
Incorporating AI-powered forecasting methods for products will help the supply chain be ready for spikes in demand, like when panic buying was witnessed during the onset of the pandemic. Other traditional forecasting modules, such as monitoring weather for the upcoming weeks and including product reviews, will also help merchandisers understand what items would be sought out most.
Employee safety:
Wearables and surveillance devices help in maintaining employee safety and help organizations in their disaster preparedness. For example, spotting a worker without gloves or face masks can be done using computer vision enabled surveillance cameras that continuously detect faces and masks from the camera feed. When not following safety rules, the system can send SMS alerts to the admins. By monitoring employee’s physical health- their heart rate and temperature, sensors can help keep an eye out for representatives who are showing indications of deteriorating health or infections.

Cost Management and Revenue Generation
Sales:
Retailers can utilize dynamic estimating models to sell from the abundant stock and make sure that a lesser number of items are going unsold over the organization. AI can assist in creating pricing models and sale strategies factoring in macroeconomic variables, operational costs, etc. Retailers can evaluate the output of the dynamic pricing algorithms to determine consumer behaviors impacting the overall market, revenues, and then plan offers, discounts, and sales based on the insights on surplus stock.
Store Layout:
In a recent MIT COVID hackathon, the winning solution was an AI engine that was able to analyze photos of hospital rooms and suggest how the room can be reconfigured to increase the number of beds. A similar AI engine can be used to analyze store architectures and restructure based on the social distancing norms and also utilize the space to its maximum capacity. A voice-enabled store guide assistance that navigates the consumer through the store to the section of interest will further minimize any unnecessary confusion during product search and minimize interactions with the store staff or other consumers.
Conclusion
The pandemic will pose new challenges in rebuilding supply chains, manufacturing networks, and customer services. Technologies like machine learning and AI can support retailers in keeping their stores profitable, can augment decision making, and keep their employees focused on initiatives that can drive customer loyalty.

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Jahnavi Agepati and Sumant Kulkarni

Posted by Jahnavi Agepati and Sumant Kulkarni

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